/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.ml;

// $example on$

import org.apache.spark.ml.clustering.BisectingKMeans;
import org.apache.spark.ml.clustering.BisectingKMeansModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;

// $example off$


/**
 * An example demonstrating bisecting k-means clustering.
 * Run with
 * <pre>
 * bin/run-example ml.JavaBisectingKMeansExample
 * </pre>
 */
public class JavaBisectingKMeansExample {
    
    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaBisectingKMeansExample")
                .getOrCreate();
        
        // $example on$
        // Loads data.
        Dataset<Row> dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt");
        
        // Trains a bisecting k-means model.
        BisectingKMeans bkm = new BisectingKMeans().setK(2).setSeed(1);
        BisectingKMeansModel model = bkm.fit(dataset);
        
        // Evaluate clustering.
        double cost = model.computeCost(dataset);
        System.out.println("Within Set Sum of Squared Errors = " + cost);
        
        // Shows the result.
        System.out.println("Cluster Centers: ");
        Vector[] centers = model.clusterCenters();
        for (Vector center : centers) {
            System.out.println(center);
        }
        // $example off$
        
        spark.stop();
    }
}
